1. Field of the Invention
The present invention relates to the field of semiconductor fabrication and, more particularly to a method and apparatus that uses multiple images in a pattern recognition process used to detect defects in the manufacture of a semiconductor device.
2. Description of the Related Art
In the semiconductor industry, there is a continuing movement towards higher integration, density and production yield, all without sacrificing throughput or processing speed. The making of today's integrated circuits (ICs) requires a complex series of fabrication, inspection and testing steps interweaved throughout the entire process to ensure the proper balance between throughput, processing speed and yield. The inspections and tests are designed to detect unwanted variations in the wafers produced, as well as in the equipment and masks used in the fabrication processes. One small defect in either the devices produced or the process itself can render a finished device inoperable.
Many of the inspection steps once done manually by skilled operators have been automated. Automated systems increase the process efficiency and reliability as the machines performing the inspection are more consistent than human operators who vary in ability and experience and are subject to fatigue when performing repetitive tasks. The automated systems also provide greater amounts of data regarding the production and equipment, which enables process engineers to both better analyze and control the process.
One such automated inspection step is known as pattern recognition or pattern inspection. Many different “patterns” appear on both the wafer and the masks used to produce the ICs. Typical pattern inspection systems are image based, as described, for example, in U.S. Pat. Nos. 4,794,646; 5,057,689; 5,641,960; and 5,659,172. In U.S. Pat. No. 4,794,646, for example, the wafer, or part thereof, is scanned and a highly resolved picture or image of the pertinent “pattern” is obtained. This pattern image is compared to other pattern images retrieved from the same or other wafers, or is compared to an ideal image stored in the inspection system database. Differences highlighted in this comparison identify possible defects in the IC or wafer.
FIG. 1 illustrates the conventional pattern recognition method 10 currently performed by today's pattern recognition or pattern inspection tools. The method 10 begins when a user places a wafer or other object to be inspected into the inspection apparatus (step 12). After scanning the wafer, the apparatus displays a “field of view” containing images from a portion of the scanned wafer (step 14). These images are to be inspected by the apparatus and thus, are referred to herein as the “inspected images.” The apparatus then displays a single pattern box within the field of view (step 16). This pattern box will be used by an operator of the apparatus to select a desired image from the inspected image. The selection is made by placing the pattern box over an image currently displayed in the field of view (step 18). The apparatus “learns” the pattern of the selected image and subsequently uses the learned pattern in a pattern recognition analysis to determine if the wafer has any defects (step 20). The use of “learns” or “learned” herein refers to the process of obtaining pattern information for the selected image and storing the information for subsequent use in a pattern recognition or critical dimension (CD) analysis. The process of learning a pattern and performing a pattern recognition analysis is well known and can be carried out in any known manner.
The method 10, however, is not without its shortcomings. Referring to FIG. 2, exemplary inspected images 50 are illustrated. In this example, the images 50 are contacts formed within a wafer being inspected. Each image 50 contains a top surface 52 of the contact and a bottom surface 54 of the contact. The bottom surfaces 52 are the desired features, which must be inspected for defects. Typical defects include under etching and over etching of the contacts and what is sometimes referred to as “closed contacts,” which are partially etched contacts.
As can be seen from FIG. 2, the top surfaces 52 are larger and much more prominent than the bottom surfaces 54. Often times, the inspection apparatus receives such strong signals from the contact top surfaces 52 that it is difficult to detect and properly inspect the contact bottom surfaces 54, which, as described above, are the desired features. By way of example, it is presumed that during the method 10 (FIG. 1) the operator placed a pattern box 70 over an image comprised of contact images 80. The selected image and its pattern found within box 70 will be hereinafter referred to as the “pattern to recognize.” Like the images 50 to be inspected, the images 80 of the pattern to be recognized contain top surfaces 82 and bottom surfaces 84. In this example, the user selected pattern to be recognized contains three contact bottom surfaces 84, each with their own expected or desired shape. The user selected pattern to be recognized also contains portions of three top surfaces 82 that are much larger than the bottom surfaces 84. The inspection apparatus learns the pattern to be recognized, which includes large signals associated with the top surfaces 82, and subsequently uses the learned pattern for comparison with the inspected images, the apparatus detects three matches 60, 62, 64.
As can be seen from FIG. 2, the three declared matches 60, 62, 64 do not contain bottom surfaces 54 that match the bottom surfaces 84 of the pattern to be recognized. Moreover, some of the matches 60, 62, 64 contain defects, e.g., under etched bottom surfaces 54a, 54b, 54c. Thus, the apparatus has incorrectly detected three matches 60, 62, 64, when there should have been zero matches and more importantly, the apparatus failed to detect three defects 54a, 54b, 54c. This anomaly occurs since the apparatus receives such strong signals from the much larger and much more prominent top surfaces 52, 82, which substantially match each other. By contrast, the apparatus receives weaker signals from the much smaller and less prominent bottom surfaces 54, 84, which do not match each other and also contain defects. Since there is much more information associated with the top surfaces 52, 82 than the bottom surfaces 54, 84, the apparatus detects the matches 60, 62, 64 based on the top surfaces 52, 82, which results in improper pattern recognition results.
Typically, the inspection apparatus will allow a user to set pattern recognition thresholds. These thresholds are designed to reduce or increase the matching percentage required between the pattern to be recognized and the inspected images. Thus, a user may set a matching threshold to 100%, in which case, the apparatus will only declare matches when the inspected images contain patterns that exactly match the pattern to be recognized. This would ensure that defective images 54a, 54b, 54c are not matched to desired and non-defective images. However, due to variations in the manufacturing process, a matching threshold of 100% would most likely lead to no desired matches or too few desired matches than are actually present. The apparatus would not detect all of the proper matches, if it detects any at all (i.e., it is under inclusive). On the other hand, if the matching threshold is set too low, e.g., 50%, then too many matches will occur. These matches will include defective images 54a, 54b, 54c whose patterns are within the matching percentage (i.e., it is over inclusive). Typically, the matching threshold is set to approximately 65% to balance between the over inclusive and under inclusive matching thresholds.
Even with a threshold setting of 65%, the conventional pattern recognition process is still unreliable. FIG. 3 illustrates another set of exemplary inspected images 50. Four sample images 90, 92, 94, 96 are also illustrated. The sample images 90, 92, 94, 96 each contain three contact images 50. The first three images 90, 92, 94 contain top surfaces 52 and bottom surfaces 54, while the fourth image 96 only contains bottom surfaces 54. The second and third images 92, 94 also contain defective bottom surfaces 92a, 94a, respectively. Using the current pattern recognition process, the first three images 90, 92, 94 would most likely match each other if one of the images 90, 92, 94 were used as a pattern to be recognized. This would happen even though the bottom surfaces 54 of the images 90, 92, 94 do not match at all and some of the surfaces 92a, 94a are defective.
It would be desirable to use the fourth sample image 96 as the pattern to be detected. As noted above, the fourth image 96 does not contain any top surfaces 52. However, the fourth sample image 96, which has bottom surfaces 54 substantially matching the bottom surfaces 54 of the first sample image 90, would not match any of the other images 90, 92, 94 because the other images contain both top and bottom surfaces 52, 54. Thus, even if it were possible to select the fourth sample image 96 as a pattern to be recognized, the pattern recognition analysis would be corrupted by the top surfaces 52, of the inspected images (e.g., images 90, 92, 94) which are not part of the desired features.
Accordingly, there is a desire and need for a pattern recognition process that filters out undesirable features from the object being inspected prior to performing a pattern recognition analysis on the object. There is also a desire and need for a pattern recognition process that allows a user to select multiple desired images of the object being inspected to be used as a pattern to be recognized during a pattern recognition analysis on the object.